Results of 1 - 3 of about 3 for Microbenchmark (0.001 sec.)
- Schema 11905
- Types It is important to use the correct type for encoding columns (e.g., BIGINT , DATE , DATETIME ). While it is always possible to use string types ( VARCHAR , etc.) to encode more specific values, this is not recommended. Strings use mor
...essing – hence, using the most restrictive types possible when creating columns is necessary to reduce memory consumption. Microbenchmark: Using Timestamps We illustrate the difference in aggregation speed using the creationDate column of the LDBC Comment tabl...e VARCHAR type and use string operations: SELECT avg(CAST(creationDate[9:10] AS INTEGER)) FROM Comment; The results of the microbenchmark are as follows: Column type Storage size Query time DATETIME 3.3 GB 0.9 s VARCHAR 5.2 GB 3.9 s The results show that using...the DATETIME value yields smaller storage sizes and faster processing. Microbenchmark: Joining on Strings We illustrate the difference caused by joining on different types by computing a self-join on the LDBC... - https://man.mtool.work/duckdb/2.0/guides/performance/schema.html
- Indexing 8259
- DuckDB has two types of indexes: zonemaps and ART indexes. Zonemaps DuckDB automatically creates zonemaps (also known as min-max indexes) for the columns of all general-purpose data types . Operations like predicate pushdown into scan opera
...it. Even an imperfect ordering will still be helpful. The best case of ordered data commonly arises with DATETIME columns. Microbenchmark: The Effect of Ordering For an example, let's repeat the microbenchmark for timestamps with an ordered timestamp column us... - https://man.mtool.work/duckdb/2.0/guides/performance/indexing.html
- File Formats 7701
- Handling Parquet Files DuckDB has advanced support for Parquet files, which includes directly querying Parquet files . When deciding on whether to query these files directly or to first load them to the database, you need to consider severa
...and zstd, are faster to decompress. You may use the parquet_metadata function to find out the compression algorithm used. Microbenchmark: Running TPC-H on a DuckDB Database vs. Parquet The queries on the TPC-H benchmark run approximately 1.1-5.0× slower on Pa...function to figure out how many row groups a Parquet file has. When writing Parquet files, use the row_group_size option. Microbenchmark: Running Aggregation Query at Different Row Group Sizes We run a simple aggregation query over Parquet files using differe... - https://man.mtool.work/duckdb/2.0/guides/performance/file_formats.html
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